# -*- coding: utf-8 -*-
'''
Created on Oct 19, 2010

@author: Peter & songting
'''
from numpy import *


def loadDataSet():
    """
    加载数据集
    :return: 文档词条列表, 文档对应的分类(0为非侮辱性, 1为侮辱性)
    """
    postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
                   ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
                   ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
                   ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
                   ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'him'],
                   ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
    classVec = [0, 1, 0, 1, 0, 1]  # 1 is abusive, 0 not
    # 返回文档词条列表, 文档对应的分类
    return postingList, classVec


def createVocabList(dataSet):
    """
    词条列表去重, 得到词汇列表
    :param dataSet: 输入的数据集
    :return: 词汇列表
    """
    vocabSet = set([])  # create empty set
    for document in dataSet:
        for word in document:
            vocabSet.add(word)
            # vocabSet = vocabSet | set(document)  # union of the two sets
    return list(vocabSet)


def setOfWords2Vec(vocabList, inputSet):
    """
    词集模型, 每个词是否出现作为特征, 将输入转化为词汇向量
    :param vocabList: 输入的词汇列表
    :param inputSet: 输入的文档
    :return: 文档的词汇向量
    """
    # 创建一个以 0 填充的文档词汇向量, 长度为词汇列表的长度
    returnVec = [0] * len(vocabList)
    ## 遍历文档, 词汇列表出现的词, 在文档词汇向量中标记为 1
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1
        else:
            print "the word: %s is not in my Vocabulary!" % word
    # 返回文档词汇向量
    return returnVec


def trainNB0(trainMatrix, trainCategory):
    """
    朴素贝叶斯分类算法训练函数
    :param trainMatrix: 文档矩阵
    :param trainCategory: 文档分类向量
    :return:
    """
    # 文档数目
    numTrainDocs = len(trainMatrix)
    # 文档中的词汇数
    numWords = len(trainMatrix[0])
    # 侮辱性文档的概率
    pAbusive = sum(trainCategory) / float(numTrainDocs)
    # 以1初始化非侮辱性的词汇列表
    p0Num = ones(numWords);
    # 以1初始化侮辱性的词汇列表
    p1Num = ones(numWords)  # change to ones()
    # 非侮辱性文档的单词量, 以2初始化
    p0Denom = 2.0;
    # 侮辱性文档的单词量, 以2初始化
    p1Denom = 2.0  # change to 2.0
    ## 遍历文档矩阵
    for i in range(numTrainDocs):
        ## 当前文档为侮辱性, 每个词的个数相加, 单词数累加
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i]
            p1Denom += sum(trainMatrix[i])
        ## 当前文档为非侮辱性, 每个词的个数相加, 单词数累加
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    # 计算词汇列表侮辱性的概率, ln()形式
    p1Vect = log(p1Num / p1Denom)  # change to log()
    # 计算词汇列表非侮辱性的概率, ln()形式
    p0Vect = log(p0Num / p0Denom)  # change to log()
    # 返回词汇列表侮辱性的概率, 非侮辱性的概率, 侮辱性文档的概率
    return p0Vect, p1Vect, pAbusive


def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    """
    朴素贝叶斯分类算法
    :param vec2Classify: 待分类的词汇向量
    :param p0Vec: 训练集中词汇表非侮辱性的概率向量
    :param p1Vec: 训练集中词汇表侮辱性的概率向量
    :param pClass1: 训练集中侮辱性文档的概率
    :return: 分类结果
    """
    # 计算词汇向量侮辱性的概率, log(p(vec|p1) * p1)
    p1 = sum(vec2Classify * p1Vec) + log(pClass1)  # element-wise mult
    # 计算词汇向量非侮辱性的概率, log(p(vec|p0) * p0)
    p0 = sum(vec2Classify * p0Vec) + log(1.0 - pClass1)
    ## P(侮辱性) > P(非侮辱性), 该文档分为侮辱性
    if p1 > p0:
        return 1
    ## P(非侮辱性) > P(侮辱性), 该文档分为非侮辱性
    else:
        return 0


def bagOfWords2VecMN(vocabList, inputSet):
    """
    词袋模型, 词出现的次数作为特征, 将输入转化为词汇向量
    :param vocabList: 输入的词汇列表
    :param inputSet: 输入的文档
    :return: 文档的词汇向量
    """
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1
    return returnVec


def testingNB():
    """
    测试朴素贝叶斯分类器
    """
    listOPosts, listClasses = loadDataSet()
    myVocabList = createVocabList(listOPosts)
    trainMat = []
    for postinDoc in listOPosts:
        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
    p0V, p1V, pAb = trainNB0(array(trainMat), array(listClasses))
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb)
    testEntry = ['stupid', 'garbage']
    thisDoc = array(setOfWords2Vec(myVocabList, testEntry))
    print testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb)


def textParse(bigString):  # input is big string, #output is word list
    import re
    listOfTokens = re.split(r'\W*', bigString)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2]


def spamTest():
    docList = [];
    classList = [];
    fullText = []
    for i in range(1, 26):
        wordList = textParse(open('email/spam/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('email/ham/%d.txt' % i).read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)  # create vocabulary
    trainingSet = range(50);
    testSet = []  # create test set
    for i in range(10):
        randIndex = int(random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del (trainingSet[randIndex])
    trainMat = [];
    trainClasses = []
    for docIndex in trainingSet:  # train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
    errorCount = 0
    for docIndex in testSet:  # classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
            errorCount += 1
            print "classification error", docList[docIndex]
    print 'the error rate is: ', float(errorCount) / len(testSet)
    # return vocabList,fullText


def calcMostFreq(vocabList, fullText):
    import operator
    freqDict = {}
    for token in vocabList:
        freqDict[token] = fullText.count(token)
    sortedFreq = sorted(freqDict.iteritems(), key=operator.itemgetter(1), reverse=True)
    return sortedFreq[:30]


def localWords(feed1, feed0):
    # import feedparser
    docList = [];
    classList = [];
    fullText = []
    minLen = min(len(feed1['entries']), len(feed0['entries']))
    for i in range(minLen):
        wordList = textParse(feed1['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)  # NY is class 1
        wordList = textParse(feed0['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)  # create vocabulary
    top30Words = calcMostFreq(vocabList, fullText)  # remove top 30 words
    for pairW in top30Words:
        if pairW[0] in vocabList: vocabList.remove(pairW[0])
    trainingSet = range(2 * minLen);
    testSet = []  # create test set
    for i in range(20):
        randIndex = int(random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del (trainingSet[randIndex])
    trainMat = [];
    trainClasses = []
    for docIndex in trainingSet:  # train the classifier (get probs) trainNB0
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
    errorCount = 0
    for docIndex in testSet:  # classify the remaining items
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
            errorCount += 1
    print 'the error rate is: ', float(errorCount) / len(testSet)
    return vocabList, p0V, p1V


def getTopWords(ny, sf):
    import operator
    vocabList, p0V, p1V = localWords(ny, sf)
    topNY = [];
    topSF = []
    for i in range(len(p0V)):
        if p0V[i] > -6.0: topSF.append((vocabList[i], p0V[i]))
        if p1V[i] > -6.0: topNY.append((vocabList[i], p1V[i]))
    sortedSF = sorted(topSF, key=lambda pair: pair[1], reverse=True)
    print "SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**SF**"
    for item in sortedSF:
        print item[0]
    sortedNY = sorted(topNY, key=lambda pair: pair[1], reverse=True)
    print "NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**NY**"
    for item in sortedNY:
        print item[0]
